The paper introduces LLM-EBG, an evolutionary framework that uses a large language model as a generative operator to automatically create optimization benchmarks. By generating unconstrained single-objective continuous minimization problems expressed as mathematical formulas, the framework can produce benchmarks that consistently favor a target algorithm over a comparison algorithm in over 80% of trials. Landscape analysis shows that these generated problems exhibit distinct geometric traits, such as sensitivity to variable scaling, reflecting the search behaviors of different optimization methods.
By Yuhiro Ono, Tomohiro Harada, Yukiya Miura
arXiv:2608. 07544v1 Announce Type: cross Abstract: Automated heuristic design (AHD) with large language models (LLMs) has produced strong heuristics for combinatorial optimization problems (COPs).
By Oguzhan Gungordu, Siheng Xiong, Faramarz Fekri
arXiv:2604.04940v3 Announce Type: replace
Abstract: Designing effective heuristics for NP-hard combinatorial optimization problems remains challenging and often requires substantial domain expertise....
By Cuong Van Duc, Minh Nguyen Dinh Tuan, Tam Vu Duc, Tung Vu Duy, Son Nguyen Van, Hanh Nguyen Thi, Binh Huynh Thi Thanh
The paper argues that measuring diversity in AI-generated content using a single scalar score is inherently ambiguous and often misleading. It reviews existing diversity metrics, demonstrates their limitations through axiomatic and empirical analyses, and introduces diversity profiles—curve-valued, condition-aware summaries that evaluate diversity across a range of thresholds, scales, exponents, or orders. These profiles reveal whether comparisons are robust across resolutions or depend on arbitrary parameter choices, offering a more transparent framework for generative AI evaluation.
By Xiuyuan Hu, Xuege Hou, Guoqing Liu, Yang Zhao, Jieran Li, Dongbiao Sun, Jos\'e Miguel Hern\'andez-Lobato, Hao Zhang, Xue Liu
arXiv:2609.00023v1 Announce Type: cross
Abstract: In this paper, we introduce ES-AHD, a novel framework that fundamentally integrates Evolution Strategy (ES) into Large Language Model (LLM)-driven Au...
By Yutao Lai, Kezhao Lai, Hai-Lin Liu, Yuping Wang, Ping Guo
arXiv:2509. 08269v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly integrated with evolutionary computation to support optimization tasks.
By Yisong Zhang, Ran Cheng, Guoxing Yi, Kay Chen Tan
arXiv:2606. 26578v1 Announce Type: new Abstract: Automating optimization modeling from natural language with large language models (LLMs) faces two key challenges.
By Qingcan Kang, Mingyang Liu, Xiaojin Fu, Shixiong Kai, Tao Zhong, Mingxuan Yuan
arXiv:2608. 03636v1 Announce Type: cross Abstract: Large language model-based automated heuristic design (LLM-AHD) has shown strong potential in discovering effective heuristics for combinatorial optimization problems.
By Haoze Lv, Ning Lu, Shengcai Liu, Shaofeng Zhang, Ke Tang
arXiv:2506. 02594v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to synthesize heuristic programs, yet most existing pipelines optimize solvers against fixed benchmark distributions.
By Ruibo Duan, Yuxin Liu, Haoran Ye, Xinyao Dong, Zhiqiang Xu, Chenglin Fan
GeLaCo is an evolutionary method for compressing large language models by collapsing layers through parametrized weight merging. It uses population-based search with a fitness function that balances similarity of residual updates and language modeling KL divergence, enabling both single and multi-objective compression. The approach yields Pareto-optimal trade-offs between compression and quality, outperforming existing methods in perplexity and generative evaluations.
By David Ponce, Thierry Etchegoyhen, Javier Del Ser
ATLAS is an embedding‑guided quality‑diversity framework that enables scaffold‑free synthesis of full algorithms for combinatorial optimization using large language models. It allows the LLM to freely choose, restructure, and control algorithm components while automatically detecting and repairing execution, interface, and feasibility failures. Across four NP‑hard problems, ATLAS outperforms state‑of‑the‑art component‑synthesis methods and remains competitive with strong human‑designed algorithms, demonstrating that a larger design space can be practically searched.
By Danial Yazdani, Mohammad Nabi Omidvar, Yuan Sun, Maksud Ibrahimov, Xiaodong Li
arXiv:2606. 04507v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become increasingly adopted in daily applications, with deep research standing out as a particularly important capability.
By Han Zhu, Chengkun Cai, Yuanfeng Song, Xing Chen, Sirui Han, Yike Guo